Mapping Quantitative Traits

Preliminaries

If you are not already familiar with the structure of these exercises, read the Introduction first.

If you have not already worked through the first part of this exercise: Quantitative Traits, begin with that first. That page has the background and information you need to fully understand this case study.

Note

Reminder: Save your work regularly.

Important

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Contact information

If you have questions about these exercises, please contact Dr. Kevin Middleton (middletonk@missouri.edu) or drop by Tucker 224.

Learning objectives

The learning objectives for this exercise are:

Describe what quantitative trait loci (QTL) are and outline QTL are identified
Explain how the contributions of many genes of small effect can be associated with a disease or condition
Differentiate Mendelian traits from threshold traits
Compare Mendelian human diseases and diseases that result from threshold traits

Introduction

In the previous exercise (Quantitative Traits), we saw how we could build up a picture of quantitative, polygenic traits from your existing understanding of Mendelian traits. Furthermore, we saw how many many genes of small effect, each of which added or subtracted a small amount to a phenotype, can produce a continuous (normal) distribution of trait values.

To this point, we have only considered how genes contribute to a quantitative trait and how many genes might contribute to a traits. What we haven’t considered yet is how scientists estimate where in the genome the associated genes are located.

Genetic Variation

Ultimately, different phenotypes – both discrete qualitative phenotypes like blood types and quantitative like heights – result from genetic variation. Many different processes lead to variation, including mutation, drift, and selection among others.

Single Nucleotide Polymorphisms

Although many methods can be used to determine the locations of traits on the genome – “mapping” – one of the most common methods in the genomic era is via single nucleotide polymorphisms (SNPs). As their name suggests, SNPs are alternate nucleotides (e.g., an A or a T) at a single location in the genome. SNPs can occur in both coding and non-coding regions of the genome (Figure 1).

Because most of the genome is identical within a species, SNPs represent a relatively small percentage of the whole genome. For example, in humans, the entire genome consists of over 6 billion base pairs, but a recent study only used 2.3 million SNPs (Yengo et al. 2018). While 2.3 million may seem like a very large number, that represents only 0.04% of the genome.

Figure 1: Single nucleotide polymorphisms are locations in the genome where two alternate nucleotides are observed. In the upper panel, two SNPs are shows, one where three individuals have a A and one with G. In the second SNP, a different combination of three individuals have G and one T. Groups of SNPs that are physically located near one another are grouped into haplotypes (lower panel). Image from Wellcome Sanger Institute.

Associating SNPs with traits

Shapiro pigeon example (dominant trait)

Figure 2: In *The Variation of Animals and Plants Under Domestication*, Charles Darwin described the array of feather phenotype in domesticated pigeons (Darwin 1868a,b). Image from Memorial University.

Figure 3: FIXME Image from (Shapiro et al. 2013).

Figure 4: FIXME Image from (Shapiro et al. 2013).

Figure 5: R = Arginine; C = Cysteine FIXME Image from (Shapiro et al. 2013).

Human Mendelian diseases are “easy” to identify

Figure 6: FIXME Image from (Visscher et al. 2012).

Figure 7: FIXME Image from (Visscher et al. 2017).

Associating QTLs with genetic variants

Case study: Investigating a newly discovered muscle mutation in mice

Figure 8

Figure 9: Distributions of the the masses of the calf muscles compared between Unaffected (red) and Mini-muscle phenotype mice (blue).

Figure 10: Comparison of the the masses of the calf muscles compared between Unaffected (red) and Mini-muscle phenotype mice (blue). The large points are the means for each group.

GWAS

GWAS Catalog

Primer (Uffelmann et al. 2021)

QTL for Human Height

Estimated to be ~700 explaining ~16% of variation in 2010 (Lango Allen et al. 2010)

Best understood quantitative trait in humans
Yet still 700 genes
Largest GWAS to date involves ~700,000 individuals described by Yengo et al. (2018)
3,290 (“near-independent”) SNPs explain ~25% of the phenotypic variation in human height among a sample of Europeans

Case Study: Threshold traits

Alzheimer (Pedersen et al. 2001)

Cardiac conditions (Walsh et al. 2020)

ASD (Grove et al. 2019)

Summary, Complex disease traits (Pal et al. 2015; Huang 2015)

Schizophrenia (~200 genes)

(Guttmacher et al. 2004; Gillis 2006; Muhrer 2014)

Why family history is one of the most important diagnostic tools in medicine

(Guttmacher et al. 2004; Gillis 2006; Muhrer 2014)

Feedback

FIXME

References

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Darwin, C. 1868b. The Variation of Animals and Plants Under Domestication, Vol. 2. John Murray, London.

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